Turtles all the way down: research challenges in user-based attestation
Bibliographic record
Abstract
A scientist once gave a public lecture describing how the Earth orbits around the sun and how the sun, in turn, orbits around the center of a collection of stars called our galaxy. At the end of the lecture, a little old lady at the back of the room got up and said: What you have told us is rubbish. The world is really a at plate supported on the back of a giant tortoise. The scientist gave a superior smile before replying, What is the tortoise standing on? You're very clever, young man, very clever, said the old lady, but it's turtles all the way down! Current trusted computing technologies allow comput-ing devices to verify each other, but in a networked world, there is no reason to trust one computing device any more than another. Treating these devices as turtles, the user who seeks a trustworthy system from which to verify oth-ers quickly realizes that it’s turtles all the way down because of the endless loop of trust dependencies. We need to provide the user with one initial turtle (the iTur-tle) which is axiomatically trustworthy, thereby breaking the dependency loop. In this paper, we present some of the research challenges involved in designing and using such an iTurtle. 1
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.098 | 0.208 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.006 | 0.022 |
| Scholarly communication | 0.020 | 0.071 |
| Open science | 0.008 | 0.009 |
| Research integrity | 0.016 | 0.016 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".